MNIST normalization. Black refactoring.

.gitignore

@@ -5,6 +5,7 @@
 .DS_Store
 
 data/*/
+implementations/*/data
 implementations/*/images
 implementations/*/saved_models
 

implementations/aae/aae.py

@@ -15,32 +15,34 @@
 import torch.nn.functional as F
 import torch
 
-os.makedirs('images', exist_ok=True)
+os.makedirs("images", exist_ok=True)
 
 parser = argparse.ArgumentParser()
-parser.add_argument('--n_epochs', type=int, default=200, help='number of epochs of training')
-parser.add_argument('--batch_size', type=int, default=64, help='size of the batches')
-parser.add_argument('--lr', type=float, default=0.0002, help='adam: learning rate')
-parser.add_argument('--b1', type=float, default=0.5, help='adam: decay of first order momentum of gradient')
-parser.add_argument('--b2', type=float, default=0.999, help='adam: decay of first order momentum of gradient')
-parser.add_argument('--n_cpu', type=int, default=8, help='number of cpu threads to use during batch generation')
-parser.add_argument('--latent_dim', type=int, default=10, help='dimensionality of the latent code')
-parser.add_argument('--img_size', type=int, default=32, help='size of each image dimension')
-parser.add_argument('--channels', type=int, default=1, help='number of image channels')
-parser.add_argument('--sample_interval', type=int, default=400, help='interval between image sampling')
+parser.add_argument("--n_epochs", type=int, default=200, help="number of epochs of training")
+parser.add_argument("--batch_size", type=int, default=64, help="size of the batches")
+parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
+parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
+parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient")
+parser.add_argument("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation")
+parser.add_argument("--latent_dim", type=int, default=10, help="dimensionality of the latent code")
+parser.add_argument("--img_size", type=int, default=32, help="size of each image dimension")
+parser.add_argument("--channels", type=int, default=1, help="number of image channels")
+parser.add_argument("--sample_interval", type=int, default=400, help="interval between image sampling")
 opt = parser.parse_args()
 print(opt)
 
 img_shape = (opt.channels, opt.img_size, opt.img_size)
 
 cuda = True if torch.cuda.is_available() else False
 
+
 def reparameterization(mu, logvar):
     std = torch.exp(logvar / 2)
     sampled_z = Variable(Tensor(np.random.normal(0, 1, (mu.size(0), opt.latent_dim))))
     z = sampled_z * std + mu
     return z
 
+
 class Encoder(nn.Module):
     def __init__(self):
         super(Encoder, self).__init__()
@@ -50,7 +52,7 @@ def __init__(self):
             nn.LeakyReLU(0.2, inplace=True),
             nn.Linear(512, 512),
             nn.BatchNorm1d(512),
-            nn.LeakyReLU(0.2, inplace=True)
+            nn.LeakyReLU(0.2, inplace=True),
         )
 
         self.mu = nn.Linear(512, opt.latent_dim)
@@ -64,6 +66,7 @@ def forward(self, img):
         z = reparameterization(mu, logvar)
         return z
 
+
 class Decoder(nn.Module):
     def __init__(self):
         super(Decoder, self).__init__()
@@ -75,14 +78,15 @@ def __init__(self):
             nn.BatchNorm1d(512),
             nn.LeakyReLU(0.2, inplace=True),
             nn.Linear(512, int(np.prod(img_shape))),
-            nn.Tanh()
+            nn.Tanh(),
         )
 
     def forward(self, z):
         img_flat = self.model(z)
         img = img_flat.view(img_flat.shape[0], *img_shape)
         return img
 
+
 class Discriminator(nn.Module):
     def __init__(self):
         super(Discriminator, self).__init__()
@@ -93,13 +97,14 @@ def __init__(self):
             nn.Linear(512, 256),
             nn.LeakyReLU(0.2, inplace=True),
             nn.Linear(256, 1),
-            nn.Sigmoid()
+            nn.Sigmoid(),
         )
 
     def forward(self, z):
         validity = self.model(z)
         return validity
 
+
 # Use binary cross-entropy loss
 adversarial_loss = torch.nn.BCELoss()
 pixelwise_loss = torch.nn.L1Loss()
@@ -117,29 +122,36 @@ def forward(self, z):
     pixelwise_loss.cuda()
 
 # Configure data loader
-os.makedirs('../../data/mnist', exist_ok=True)
+os.makedirs("../../data/mnist", exist_ok=True)
 dataloader = torch.utils.data.DataLoader(
-    datasets.MNIST('../../data/mnist', train=True, download=True,
-                   transform=transforms.Compose([
-                       transforms.Resize(opt.img_size),
-                       transforms.ToTensor(),
-                       transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
-                   ])),
-    batch_size=opt.batch_size, shuffle=True)
+    datasets.MNIST(
+        "../../data/mnist",
+        train=True,
+        download=True,
+        transform=transforms.Compose(
+            [transforms.Resize(opt.img_size), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]
+        ),
+    ),
+    batch_size=opt.batch_size,
+    shuffle=True,
+)
 
 # Optimizers
-optimizer_G = torch.optim.Adam( itertools.chain(encoder.parameters(), decoder.parameters()),
-                                lr=opt.lr, betas=(opt.b1, opt.b2))
+optimizer_G = torch.optim.Adam(
+    itertools.chain(encoder.parameters(), decoder.parameters()), lr=opt.lr, betas=(opt.b1, opt.b2)
+)
 optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
 
 Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
 
+
 def sample_image(n_row, batches_done):
     """Saves a grid of generated digits"""
     # Sample noise
-    z = Variable(Tensor(np.random.normal(0, 1, (n_row**2, opt.latent_dim))))
+    z = Variable(Tensor(np.random.normal(0, 1, (n_row ** 2, opt.latent_dim))))
     gen_imgs = decoder(z)
-    save_image(gen_imgs.data, 'images/%d.png' % batches_done, nrow=n_row, normalize=True)
+    save_image(gen_imgs.data, "images/%d.png" % batches_done, nrow=n_row, normalize=True)
+
 
 # ----------
 #  Training
@@ -165,8 +177,9 @@ def sample_image(n_row, batches_done):
         decoded_imgs = decoder(encoded_imgs)
 
         # Loss measures generator's ability to fool the discriminator
-        g_loss =    0.001 * adversarial_loss(discriminator(encoded_imgs), valid) + \
-                    0.999 * pixelwise_loss(decoded_imgs, real_imgs)
+        g_loss = 0.001 * adversarial_loss(discriminator(encoded_imgs), valid) + 0.999 * pixelwise_loss(
+            decoded_imgs, real_imgs
+        )
 
         g_loss.backward()
         optimizer_G.step()
@@ -188,8 +201,10 @@ def sample_image(n_row, batches_done):
         d_loss.backward()
         optimizer_D.step()
 
-        print ("[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]" % (epoch, opt.n_epochs, i, len(dataloader),
-                                                            d_loss.item(), g_loss.item()))
+        print(
+            "[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]"
+            % (epoch, opt.n_epochs, i, len(dataloader), d_loss.item(), g_loss.item())
+        )
 
         batches_done = epoch * len(dataloader) + i
         if batches_done % opt.sample_interval == 0:

implementations/acgan/acgan.py

@@ -14,41 +14,43 @@
 import torch.nn.functional as F
 import torch
 
-os.makedirs('images', exist_ok=True)
+os.makedirs("images", exist_ok=True)
 
 parser = argparse.ArgumentParser()
-parser.add_argument('--n_epochs', type=int, default=200, help='number of epochs of training')
-parser.add_argument('--batch_size', type=int, default=64, help='size of the batches')
-parser.add_argument('--lr', type=float, default=0.0002, help='adam: learning rate')
-parser.add_argument('--b1', type=float, default=0.5, help='adam: decay of first order momentum of gradient')
-parser.add_argument('--b2', type=float, default=0.999, help='adam: decay of first order momentum of gradient')
-parser.add_argument('--n_cpu', type=int, default=8, help='number of cpu threads to use during batch generation')
-parser.add_argument('--latent_dim', type=int, default=100, help='dimensionality of the latent space')
-parser.add_argument('--n_classes', type=int, default=10, help='number of classes for dataset')
-parser.add_argument('--img_size', type=int, default=32, help='size of each image dimension')
-parser.add_argument('--channels', type=int, default=1, help='number of image channels')
-parser.add_argument('--sample_interval', type=int, default=400, help='interval between image sampling')
+parser.add_argument("--n_epochs", type=int, default=200, help="number of epochs of training")
+parser.add_argument("--batch_size", type=int, default=64, help="size of the batches")
+parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
+parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
+parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient")
+parser.add_argument("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation")
+parser.add_argument("--latent_dim", type=int, default=100, help="dimensionality of the latent space")
+parser.add_argument("--n_classes", type=int, default=10, help="number of classes for dataset")
+parser.add_argument("--img_size", type=int, default=32, help="size of each image dimension")
+parser.add_argument("--channels", type=int, default=1, help="number of image channels")
+parser.add_argument("--sample_interval", type=int, default=400, help="interval between image sampling")
 opt = parser.parse_args()
 print(opt)
 
 cuda = True if torch.cuda.is_available() else False
 
+
 def weights_init_normal(m):
     classname = m.__class__.__name__
-    if classname.find('Conv') != -1:
+    if classname.find("Conv") != -1:
         torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
-    elif classname.find('BatchNorm2d') != -1:
+    elif classname.find("BatchNorm2d") != -1:
         torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
         torch.nn.init.constant_(m.bias.data, 0.0)
 
+
 class Generator(nn.Module):
     def __init__(self):
         super(Generator, self).__init__()
 
         self.label_emb = nn.Embedding(opt.n_classes, opt.latent_dim)
 
-        self.init_size = opt.img_size // 4 # Initial size before upsampling
-        self.l1 = nn.Sequential(nn.Linear(opt.latent_dim, 128*self.init_size**2))
+        self.init_size = opt.img_size // 4  # Initial size before upsampling
+        self.l1 = nn.Sequential(nn.Linear(opt.latent_dim, 128 * self.init_size ** 2))
 
         self.conv_blocks = nn.Sequential(
             nn.BatchNorm2d(128),
@@ -61,7 +63,7 @@ def __init__(self):
             nn.BatchNorm2d(64, 0.8),
             nn.LeakyReLU(0.2, inplace=True),
             nn.Conv2d(64, opt.channels, 3, stride=1, padding=1),
-            nn.Tanh()
+            nn.Tanh(),
         )
 
     def forward(self, noise, labels):
@@ -71,15 +73,14 @@ def forward(self, noise, labels):
         img = self.conv_blocks(out)
         return img
 
+
 class Discriminator(nn.Module):
     def __init__(self):
         super(Discriminator, self).__init__()
 
         def discriminator_block(in_filters, out_filters, bn=True):
             """Returns layers of each discriminator block"""
-            block = [   nn.Conv2d(in_filters, out_filters, 3, 2, 1),
-                        nn.LeakyReLU(0.2, inplace=True),
-                        nn.Dropout2d(0.25)]
+            block = [nn.Conv2d(in_filters, out_filters, 3, 2, 1), nn.LeakyReLU(0.2, inplace=True), nn.Dropout2d(0.25)]
             if bn:
                 block.append(nn.BatchNorm2d(out_filters, 0.8))
             return block
@@ -92,13 +93,11 @@ def discriminator_block(in_filters, out_filters, bn=True):
         )
 
         # The height and width of downsampled image
-        ds_size = opt.img_size // 2**4
+        ds_size = opt.img_size // 2 ** 4
 
         # Output layers
-        self.adv_layer = nn.Sequential( nn.Linear(128*ds_size**2, 1),
-                                        nn.Sigmoid())
-        self.aux_layer = nn.Sequential( nn.Linear(128*ds_size**2, opt.n_classes),
-                                        nn.Softmax())
+        self.adv_layer = nn.Sequential(nn.Linear(128 * ds_size ** 2, 1), nn.Sigmoid())
+        self.aux_layer = nn.Sequential(nn.Linear(128 * ds_size ** 2, opt.n_classes), nn.Softmax())
 
     def forward(self, img):
         out = self.conv_blocks(img)
@@ -108,6 +107,7 @@ def forward(self, img):
 
         return validity, label
 
+
 # Loss functions
 adversarial_loss = torch.nn.BCELoss()
 auxiliary_loss = torch.nn.CrossEntropyLoss()
@@ -127,15 +127,19 @@ def forward(self, img):
 discriminator.apply(weights_init_normal)
 
 # Configure data loader
-os.makedirs('../../data/mnist', exist_ok=True)
+os.makedirs("../../data/mnist", exist_ok=True)
 dataloader = torch.utils.data.DataLoader(
-    datasets.MNIST('../../data/mnist', train=True, download=True,
-                   transform=transforms.Compose([
-                        transforms.Resize(opt.img_size),
-                        transforms.ToTensor(),
-                        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
-                   ])),
-    batch_size=opt.batch_size, shuffle=True)
+    datasets.MNIST(
+        "../../data/mnist",
+        train=True,
+        download=True,
+        transform=transforms.Compose(
+            [transforms.Resize(opt.img_size), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]
+        ),
+    ),
+    batch_size=opt.batch_size,
+    shuffle=True,
+)
 
 # Optimizers
 optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
@@ -144,15 +148,17 @@ def forward(self, img):
 FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
 LongTensor = torch.cuda.LongTensor if cuda else torch.LongTensor
 
+
 def sample_image(n_row, batches_done):
     """Saves a grid of generated digits ranging from 0 to n_classes"""
     # Sample noise
-    z = Variable(FloatTensor(np.random.normal(0, 1, (n_row**2, opt.latent_dim))))
+    z = Variable(FloatTensor(np.random.normal(0, 1, (n_row ** 2, opt.latent_dim))))
     # Get labels ranging from 0 to n_classes for n rows
     labels = np.array([num for _ in range(n_row) for num in range(n_row)])
     labels = Variable(LongTensor(labels))
     gen_imgs = generator(z, labels)
-    save_image(gen_imgs.data, 'images/%d.png' % batches_done, nrow=n_row, normalize=True)
+    save_image(gen_imgs.data, "images/%d.png" % batches_done, nrow=n_row, normalize=True)
+
 
 # ----------
 #  Training
@@ -186,8 +192,7 @@ def sample_image(n_row, batches_done):
 
         # Loss measures generator's ability to fool the discriminator
         validity, pred_label = discriminator(gen_imgs)
-        g_loss = 0.5 * (adversarial_loss(validity, valid) + \
-                        auxiliary_loss(pred_label, gen_labels))
+        g_loss = 0.5 * (adversarial_loss(validity, valid) + auxiliary_loss(pred_label, gen_labels))
 
         g_loss.backward()
         optimizer_G.step()
@@ -200,13 +205,11 @@ def sample_image(n_row, batches_done):
 
         # Loss for real images
         real_pred, real_aux = discriminator(real_imgs)
-        d_real_loss =  (adversarial_loss(real_pred, valid) + \
-                        auxiliary_loss(real_aux, labels)) / 2
+        d_real_loss = (adversarial_loss(real_pred, valid) + auxiliary_loss(real_aux, labels)) / 2
 
         # Loss for fake images
         fake_pred, fake_aux = discriminator(gen_imgs.detach())
-        d_fake_loss =  (adversarial_loss(fake_pred, fake) + \
-                        auxiliary_loss(fake_aux, gen_labels)) / 2
+        d_fake_loss = (adversarial_loss(fake_pred, fake) + auxiliary_loss(fake_aux, gen_labels)) / 2
 
         # Total discriminator loss
         d_loss = (d_real_loss + d_fake_loss) / 2
@@ -219,9 +222,10 @@ def sample_image(n_row, batches_done):
         d_loss.backward()
         optimizer_D.step()
 
-        print ("[Epoch %d/%d] [Batch %d/%d] [D loss: %f, acc: %d%%] [G loss: %f]" % (epoch, opt.n_epochs, i, len(dataloader),
-                                                            d_loss.item(), 100 * d_acc,
-                                                            g_loss.item()))
+        print(
+            "[Epoch %d/%d] [Batch %d/%d] [D loss: %f, acc: %d%%] [G loss: %f]"
+            % (epoch, opt.n_epochs, i, len(dataloader), d_loss.item(), 100 * d_acc, g_loss.item())
+        )
         batches_done = epoch * len(dataloader) + i
         if batches_done % opt.sample_interval == 0:
             sample_image(n_row=10, batches_done=batches_done)